- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Flexible Rate Allocation for Local Binary Feature Compression
摘要： Numerous real-time applications in computer vision rely on finding correspondences between local binary features. In many mobile scenarios, the visual information captured at a sensor node needs to be transmitted to a processing server, which is capable of storing the visual information or executing a complex analysis task. However, not necessarily all the visual information need to be transmitted. In this paper, we present a rate allocation scheme that is capable of categorizing features into classes according to their usefulness and select the amount of data spent on each class to maximize the overall performance of a computer vision task. We demonstrate the approach using ORB, BRISK, and FREAK features and show the improvements on a homography estimation task.
关键词： Visual features,Bag-of-Words,coding,feature coding,ATC
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Combination of Band Selection and Weighted Spatial-Spectral Method for Hyperspectral Image Classification
摘要： In this paper we propose a new method for land cover classification in hyperspectral remote sensing images by combining band selection with weighted spatial-spectral feature fusion. Spectral information for each pixel is represented by a spectral curve over all the bands. Spatial information is represented by a Bag of visual Words model within a small region around each pixel. A cluster-based band selection method is used before spatial feature extraction to reduce the computation complexity. Then spectral and spatial feature weights are learnt under a Support Vector Machine framework, obtaining a balance between the two basis features for each class. Classification results on three popular hyperspectral remote sensing images demonstrate that the proposed method can yield a higher accuracy and a lower false alarm rate compared with the other similar classifiers.
关键词： bag of words,Hyperspectral image,spatial-spectral,classification,band select
更新于2025-09-09 09:28:46